activity
20242026
collaborators

8 papers

cs.CV2026

Relightable Gaussian Splatting for Virtual Production Using Image-Based Illumination

Adrian Azzarelli, Nantheera Anantrasirichai, James Pollock +1

Virtual production (VP) use LED walls to provide both background imagery and image-based lighting. While this enables on-set compositing, it couples lighting to background and scen…

cs.CV2026

Splatography: Sparse multi-view dynamic Gaussian Splatting for filmmaking challenges

Adrian Azzarelli, Nantheera Anantrasirichai, David R Bull

Deformable Gaussian Splatting (GS) accomplishes photorealistic dynamic 3-D reconstruction from dense multi-view video (MVV) by learning to deform a canonical GS representation. How…

cs.CV2025

ViVo: A Dataset for Volumetric Video Reconstruction and Compression

Adrian Azzarelli, Ge Gao, Ho Man Kwan +4

As research on neural volumetric video reconstruction and compression flourishes, there is a need for diverse and realistic datasets, which can be used to develop and validate reco…

cs.CV2025

AquaNeRF: Neural Radiance Fields in Underwater Media with Distractor Removal

Luca Gough, Adrian Azzarelli, Fan Zhang +1

Neural radiance field (NeRF) research has made significant progress in modeling static video content captured in the wild. However, current models and rendering processes rarely co…

cs.CV2025

Reviewing Intelligent Cinematography: AI research for camera-based video production

Adrian Azzarelli, Nantheera Anantrasirichai, David R Bull

This paper offers the first comprehensive review of artificial intelligence (AI) research in the context of real camera content acquisition for entertainment purposes and is aimed…

cs.CV2024

Exploring Dynamic Novel View Synthesis Technologies for Cinematography

Adrian Azzarelli, Nantheera Anantrasirichai, David R Bull

Novel view synthesis (NVS) has shown significant promise for applications in cinematographic production, particularly through the exploitation of Neural Radiance Fields (NeRF) and…